Combinatorial problems and various discrete solution systems are well known in the art of operations research and the art of optimization generally. (T. H. Cormen, C. E. Leiserson and R. L. Rivest, Introduction to Algorithms, MIT Press, Massachusetts, 1990; R. Fletcher, Practical Methods of Optimization, 2nd edition, John Wiley and Sons, Chichester, 1987; Hillier, F. S., and Lieberman, G. J., Introduction to Operations Research, McGraw-Hill, New York, 1995; R. G. Parker and R. L Rardin, Discrete Optimization, Academic Press, Boston, 1988; Handbook of Combinatorial Optimization, vol. 1, D.-Z. Du and P. M. Pardalos, eds., Kluwer Academic Publishers, Drodrecht, 1998.)
In many business applications, however, combinatorial optimization presents substantial challenges in that the computational resources and time required to develop an acceptable solution are typically unacceptable. Several approaches to providing a rapid solution to a combinatorial problem include the use of parallel digital processors to solve such problems as articulated in U.S. Pat. No. 5,537,593 issued to FMC Corporation™ on Jul. 16, 1996. IBM™ Corporation has received numerous patents on various methods of optimizing inventory and materials requirements planning (MRP), which are basically resource allocation problems, such as U.S. Pat. No. 5,216,593 issued Jun. 1, 1993. Others have received patents on neurocomputers suitable for solving an optimum combination problem. Nevertheless, the technical problem of developing a highly efficient and cost effective system and method for solving hard combinatorial problems remains.
In many business applications, traditional attempted solutions to technical combinatorial problems involving resource allocation rely on periodic static data, acquired after-the-fact, are based on cumbersome, dueling spreadsheets, and require extensive time to run scenarios of multiple resource allocation options. What is desired is an improved system and method to solve these technical combinatorial problems to achieve optimal resource allocation.